Optimal Drug Regimens for TB: An Integrated Computational/Experimental Approach

描述（由申请人提供）：本 K25 申请中的候选人具有理论物理和计算机科学背景。该候选人的长期职业目标是将生物科学领域的严格培训与其定量和计算机技能相结合，以加速治疗结核病 (TB) 的后期药物开发过程。近期目标是开发改进的计算方法和工具，以确定临床前开发阶段针对结核病的新的最佳联合疗法。该职业发展奖旨在支持一项研究和培训计划，其中包括强化课程、指导阅读、参加讲习班、会议和研讨会，以及一项研究计划，该计划为结核病治疗中宿主-药物-病原体相互作用提供了有根据的理解。 该候选人将得到科罗拉多州立大学 (CSU) 微生物学、免疫学和病理学系的支持。该提案的导师和合作者包括分枝杆菌研究实验室（MRL）、环境与放射健康科学系、化学与生物工程系、临床科学系、兽医与生物医学科学学院的教师。 CSU 是传染病研究领域的领导者，MRL 拥有 19 名教员和 100 多名全职工作人员，是世界上最大的专注于分枝杆菌研究的团体，为进入结核病研究领域以及该项目的成功提供了理想的环境。 为了为结核病新药方案的临床试验测试提供指导，显然需要一种有效的方法来确定临床前开发阶段的最佳组合方案。虽然传统的药代动力学/药效学 (PK/PD) 方法已被证明可用于确定最佳的单一药物抗菌治疗方案，但它们的数据密集型，并且对于治疗结核病所需的 3 种和 4 种药物治疗方案进行系统、彻底的评估的效用有限。因此，本研究计划的目的和目标是开发和演示计算框架的使用，该框架为治疗小鼠结核分枝杆菌感染提供最佳的组合药物剂量方案。所提出的框架是基于生理学的数学模型、贝叶斯推理、有针对性的实验研究和严格的剂量优化方法的新颖整合。该提案的具体目标是：（1）开发小鼠结核病感染模型中宿主-药物-病原体相互作用和药物剂量优化的计算框架，以及（2）展示计算框架在小鼠结核病感染模型中确定最佳多药治疗方案的用途。虽然我们寻求使用当前的一线抗结核药物（异烟肼、利福平、吡嗪酰胺）来确定该提案的可行性，但该项目的最终目标是将该方法应用于较新的抗结核药物，以便预测临床试验中测试的最佳方案。

DESCRIPTION (provided by applicant): The candidate in this K25 application has a background in theoretical physics and computer science. The long term career goal of this candidate is to combine rigorous training in the field of bioscience with his quantitative and computer skills in order to accelerate the late-stage drug development process for the treatment of tuberculosis (TB). The immediate goal is to develop improved computational methods and tools to determine new optimal combination therapies for TB in the preclinical stage of development. This Career Development Award is requested to support a research and training program that includes intensive coursework, mentored readings, attendance at workshops, meetings and seminars, and a research plan that provides for a well-founded understanding of the host-drug-pathogen interactions for the treatment of TB. The candidate will be supported by the Department of Microbiology, Immunology and Pathology at Colorado State University (CSU). The mentors and collaborators for this proposal include faculty from the Mycobacteria Research Laboratories (MRL), the Department of Environmental & Radiological Health Sciences, Chemical & Biological Engineering, and the Department of Clinical Sciences, College of Veterinary Medicine and Biomedical Sciences. CSU is a leader in infectious disease research, and the MRL, with their 19 faculty and over 100 full-time staff, is the worlds largest group focused on mycobacterial research, providing an ideal environment to be introduced to the field of TB research, and for the success of this project. In order to provide guidance for clinical trial testing of new drug regimens for TB, there is a clear need for an efficient method to determine optimal combination regimens in the preclinical stage of development. While conventional pharmacokinetic/pharmacodynamic (PK/PD) methods have proven useful for the determination of optimal single drug antimicrobial regimens, they are data intensive and have limited utility for a systematic and thorough evaluation of the 3- and 4-drug regimens required to treat TB. The objective and goal of this research plan is therefore to develop and demonstrate the use of a computational framework which provides optimal combination drug dosage regimens for treatment of Mycobacterium tuberculosis infection in mice. The proposed framework is a novel integration of physiologically based mathematical modeling, Bayesian inference, targeted experimental studies, and a rigorous method for dose optimization. The specific aims of this proposal are to: (1) develop the computational framework for host-drug-pathogen interactions and drug dose optimization in mouse TB infection models, and (2) demonstrate the use of the computational framework for the determination of an optimal multidrug regimen in mouse TB infection models. While we seek to establish the feasibility of the proposal using current front-line anti-TB drugs (isoniazid, rifampin, pyrazinamide), the ultimate aim of this project is the application of the methodology to the newer anti-TB drugs in order to render predictions of optimal regimens for testing in clinical trials.